Translator Disclaimer
14 March 2003 An expert approach to archaeological sites location through remote sensing information
Author Affiliations +
It is commonly accepted that the spatial distribution of archaeological sites is largely dependent on the characteristics of the environment. Hence, during the last decades, many studies have been focused on selecting environmental characteristics that can be used successfully in predicting unknown site locations. In previous studies the authors, using experimental tests and ground surveys, analysed many environmental factors and identified the most important ones for defining the inclination of an area to settling. Some of these could be obtained from cartography and Digital Elevation Models (DEM), whereas others were extracted from remote sensing imagery. In this work, Landsat ETM and IKONOS-2 satellite data were used to obtain environmental information useful in predicting new archaeological sites using an expert euristic approach. The information obtained from satellite data, plus a few other environmental descriptors, was used to build a predictive archaeological model that characterised an inclination to settle in the test area (region of Lucania in Southern Italy). The map of settlement tendency thus obtained, which was verified during few ground surveys, led to the identification of more than one hundred new archaeological sites, with a prediction accuracy greater than 80%. The environmental characteristics of the new archaeological locations were then statistically analysed and their effectiveness was evaluated. The results demonstrated that the integration of remotely-sensed information within an archaeological model greatly enhanced the capabilities for searching out identifying new archaeological settlements.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Roberto Carla, Maria Jacoli, Giuliana Profeti, and Valerio Venturi "An expert approach to archaeological sites location through remote sensing information", Proc. SPIE 4886, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, (14 March 2003);


Back to Top